Improving the coverage area and flake size of ReS 2 through machine learning in APCVD.

Autor: Flores Salazar M; Centro de Física Aplicada y Tecnología Avanzada, Universidad Nacional Autónoma de México, A.P. 1-1010, Querétaro, Qro., C.P. 76000, Mexico., Frausto-Avila CM; Centro de Física Aplicada y Tecnología Avanzada, Universidad Nacional Autónoma de México, A.P. 1-1010, Querétaro, Qro., C.P. 76000, Mexico., de Jesús Bautista JA; Centro de Física Aplicada y Tecnología Avanzada, Universidad Nacional Autónoma de México, A.P. 1-1010, Querétaro, Qro., C.P. 76000, Mexico., Polumati G; Department of Electrical and Electronics Engineering, BITS Pilani Hyderabad Campus, Hyderabad 500078, India., Muñiz Martínez BA; Cinvestav Unidad Queretaro, Qro., Queretaro 76230, Mexico., Sekhar Reddy KC; Department of Electrical and Electronics Engineering, BITS Pilani Hyderabad Campus, Hyderabad 500078, India., Hernández-Vázquez MÁ; Centro de Investigación, Innovación y Desarrollo Tecnológico (CIIDETEC-UVM), Universidad del Valle de México, Querétaro 76230, Mexico., Strupiechonski E; Centro de Ingeniería y Desarrollo Industrial, Avenida Pie de la Cuesta #702, Santiago de Querétaro, 76125 Querétaro, Mexico., Sahatiya P; Department of Electrical and Electronics Engineering, BITS Pilani Hyderabad Campus, Hyderabad 500078, India., Quiroz-Juárez MA; Centro de Física Aplicada y Tecnología Avanzada, Universidad Nacional Autónoma de México, A.P. 1-1010, Querétaro, Qro., C.P. 76000, Mexico., De Luna Bugallo A; Centro de Física Aplicada y Tecnología Avanzada, Universidad Nacional Autónoma de México, A.P. 1-1010, Querétaro, Qro., C.P. 76000, Mexico.
Jazyk: angličtina
Zdroj: Nanotechnology [Nanotechnology] 2024 Oct 04; Vol. 35 (50). Date of Electronic Publication: 2024 Oct 04.
DOI: 10.1088/1361-6528/ad7e2e
Abstrakt: Machine learning is playing a crucial role in optimizing material synthesis, particularly in scenarios where several parameters related to growth exhibit different and significant outcomes. An example of such complexity is the growth of atomically thin semiconductors through chemical vapor deposition (CVD), where multiple parameters can influence the thermodynamics and reaction kinetics involved in the synthesis. Herein, we performed a set of orthogonal experiments, varying the key parameters such as temperature, carries gas flux and precursor position to identify the optimal conditions for maximizing covered area and the size of rhenium disulfide (ReS 2 ) crystals. The experimental results were used to establish correlations among the three thermodynamic variables through an artificial neural network. Contour plots were then generated to visualize the impact on the coverage and flake size of the crystals. This study demonstrates the capability of machine learning to enhance the potential of CVD-growth for the integration of 2D semiconductors like ReS 2 at larger scales.
(Creative Commons Attribution license.)
Databáze: MEDLINE
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